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      Post-licensure surveillance of quadrivalent inactivated influenza (IIV4) vaccine in the United States, Vaccine Adverse Event Reporting System (VAERS), July 1, 2013−May 31, 2015

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          Abstract

          Background

          Quadrivalent inactivated influenza vaccines (IIV4) were first available for use during 2013−14 influenza season for individuals aged ≥6 months. IIV4 is designed to protect against four different flu viruses; two influenza A viruses and two influenza B viruses.

          Methods

          We searched the Vaccine Adverse Event Reporting System (VAERS) for US reports after IIV4 and trivalent inactivated influenza vaccine (IIV3) from 7/1/2013–5/31/2015. Medical records were requested for non-manufacturer reports classified as serious (i.e. death, hospitalization, prolonged hospitalization, life-threatening illness, permanent disability). The review included automated data analysis, clinical review of all serious reports, reports of special interest, and empirical Bayesian data mining.

          Results

          VAERS received 1,838 IIV4 reports; 512 (28%) in persons aged 6 months–17 years of which 42 (8.2%) were serious reports; 1,265 (69%) in persons aged >18 years of which 84 (6.6%) were serious reports; two in children <6 months and 59 in persons of unknown age. Injection site erythema (24%), fever (14%) and injection site swelling (17%) were the most frequent adverse events among persons aged 6 months–17 years, while injection site pain (16%), pain (15%) and pain in extremity (13%) were the most frequent among persons aged 18−64 years given the vaccine alone. Among non-death serious reports, injection site reactions, constitutional symptoms, Guillain-Barré syndrome, seizures, and anaphylaxis were the most frequently reported adverse events. Data mining detected disproportional reporting for incorrect vaccine administration with no associated adverse events. Adverse events following IIV4 reported to VAERS were similar to those following IIV3.

          Conclusions

          In our review of VAERS reports, IIV4 had a similar safety profile to IIV3. Most of the reported AEs were non-serious. Our findings are consistent with data from pre-licensure studies of IIV4.

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          Population Incidence of Guillain-Barré Syndrome: A Systematic Review and Meta-Analysis

          James J. Sejvar, J. SEJVAR, Andrew L. Baughman … (2011)
          Population incidence of Guillain-Barré syndrome (GBS) is required to assess changes in GBS epidemiology, but published estimates of GBS incidence vary greatly depending on case ascertainment, definitions, and sample size. We performed a meta-analysis of articles on GBS incidence by searching Medline (1966–2009), Embase (1988–2009), Cinahl (1981–2009) and CABI (1973–2009) as well as article bibliographies. We included studies from North America and Europe with at least 20 cases, and used population-based data, subject matter experts to confirm GBS diagnosis, and an accepted GBS case definition. With these data, we fitted a random-effects negative binomial regression model to estimate age-specific GBS incidence. Of 1,683 nonduplicate citations, 16 met the inclusion criteria, which produced 1,643 cases and 152.7 million person-years of follow-up. GBS incidence increased by 20% for every 10-year increase in age; the risk of GBS was higher for males than females. The regression equation for calculating the average GBS rate per 100,000 person-years as a function of age in years was exp[–12.0771 + 0.01813(age in years)] × 100,000. Our findings provide a robust estimate of background GBS incidence in Western countries. Our regression model may be used in comparable populations to estimate the background age-specific rate of GBS incidence for future studies.
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            Guillain-Barre syndrome following vaccination in the National Influenza Immunization Program, United States, 1976--1977.

            LAWRENCE B. SCHONBERGER, DENNIS BREGMAN, JOHN SULLIVAN-BOLYAI … (1979)
            Because of an increase in the number of reports of Guillian-Barre syndrome (GBS) following A/New Jersey influenza vaccination, the National Influenza Immunization Program was suspended December 16, 1976 and nationwide surveillance for GBS was begun. This surveillance uncovered a total of 1098 patients with onset of GBS from October 1, 1976, to January 31, 1977, from all 50 states, District of Columbia, and Puerto Rico. A total of 532 patients had recently received an A/New Jersey influenza vaccination prior to their onset of GBS (vaccinated cases), and 15 patients received a vaccination after their onset of GBS. Five hundred forty-three patients had not been recently vaccinated with A/New Jersey influenza vaccine and the vaccination status for 8 was unknown. Epidemiologic evidence indicated that many cases of GBS were related to vaccination. When compared to the unvaccinated population, the vaccinated population had a significantly elevated attack rate in every adult age group. The estimated attributable risk of vaccine-related GBS in the adult population was just under one case per 100,000 vaccinations. The period of increased risk was concentrated primarily within the 5-week period after vaccination, although it lasted for approximately 9 or 10 weeks.
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              Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA's spontaneous reports database.

              Mileni Machado, Matthew T. O'Neill, Ana Szarfman (2001)
              Since 1998, the US Food and Drug Administration (FDA) has been exploring new automated and rapid Bayesian data mining techniques. These techniques have been used to systematically screen the FDA's huge MedWatch database of voluntary reports of adverse drug events for possible events of concern. The data mining method currently being used is the Multi-Item Gamma Poisson Shrinker (MGPS) program that replaced the Gamma Poisson Shrinker (GPS) program we originally used with the legacy database. The MGPS algorithm, the technical aspects of which are summarised in this paper, computes signal scores for pairs, and for higher-order (e.g. triplet, quadruplet) combinations of drugs and events that are significantly more frequent than their pair-wise associations would predict. MGPS generates consistent, redundant, and replicable signals while minimising random patterns. Signals are generated without using external exposure data, adverse event background information, or medical information on adverse drug reactions. The MGPS interface streamlines multiple input-output processes that previously had been manually integrated. The system, however, cannot distinguish between already-known associations and new associations, so the reviewers must filter these events. In addition to detecting possible serious single-drug adverse event problems, MGPS is currently being evaluated to detect possible synergistic interactions between drugs (drug interactions) and adverse events (syndromes), and to detect differences among subgroups defined by gender and by age, such as paediatrics and geriatrics. In the current data, only 3.4% of all 1.2 million drug-event pairs ever reported (with frequencies > or = 1) generate signals [lower 95% confidence interval limit of the adjusted ratios of the observed counts over expected (O/E) counts (denoted EB05) of > or = 2]. The total frequency count that contributed to signals comprised 23% (2.4 million) of the total number, 10.4 million of drug-event pairs reported, greatly facilitating a more focused follow-up and evaluation. The algorithm provides an objective, systematic view of the data alerting reviewers to critically important, new safety signals. The study of signals detected by current methods, signals stored in the Center for Drug Evaluation and Research's Monitoring Adverse Reports Tracking System, and the signals regarding cerivastatin, a cholesterol-lowering drug voluntarily withdrawn from the market in August 2001, exemplify the potential of data mining to improve early signal detection. The operating characteristics of data mining in detecting early safety signals, exemplified by studying a drug recently well characterised by large clinical trials confirms our experience that the signals generated by data mining have high enough specificity to deserve further investigation. The application of these tools may ultimately improve usage recommendations.
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                Author and article information

                Contributors
                Penina Haber
                Journal
                Journal ID (nlm-ta): Vaccine
                Journal ID (iso-abbrev): Vaccine
                Title: Vaccine
                Publisher: Elsevier Science
                ISSN (Print): 0264-410X
                ISSN (Electronic): 1873-2518
                Publication date PMC-release: 11 May 2016
                Publication date (Print): 11 May 2016
                Volume: 34
                Issue: 22
                Pages: 2507-2512
                Affiliations
                [a ]Immunization Safety Office, Division of Healthcare Quality Promotion, National Center Emerging and Zoonotic Infectious Diseases (NCEZID), Centers for Disease Control and Prevention (CDC) , 1600 Clifton Rd NE, Atlanta GA 30329, United States
                [b ]Food and Drug Administration, Center for Biologics Evaluation and Research, Office of Biostatistics and Epidemiology, Silver Spring, MD, United States
                Author notes
                [* ]Corresponding author. Tel.: +1 404 498 0676; fax: +1 404 498 0666. PHaber@ 123456cdc.gov
                Article
                Publisher ID: S0264-410X(16)30031-7
                DOI: 10.1016/j.vaccine.2016.03.048
                PMC ID: 4916262
                PubMed ID: 27015735
                SO-VID: 0fa043c4-a85c-4fee-89e1-3c91840df391
                History
                Date received : 22 January 2016
                Date revision received : 14 March 2016
                Date accepted : 15 March 2016
                Categories
                Subject: Article

                ScienceOpen disciplines: Infectious disease & Microbiology
                Keywords: vaccine safety,post-licensure surveillance,quadrivalent inactivated influenza vaccines
                Data availability:
                ScienceOpen disciplines: Infectious disease & Microbiology
                Keywords: vaccine safety, post-licensure surveillance, quadrivalent inactivated influenza vaccines

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